Exploring the climatic effects of agricultural irrigation and potential mechanisms is essential for guiding sustainable irrigation development. However, the impacts of irrigation on the climate of the humid Middle-Lower Yangtze Plain (MLYP) region in China, a global hotspots of irrigation, remain unclear. The region employs intricate rotation patterns, with paddy as the primary crop cultivated in inundated fields. Due to the simplification of differentiated irrigation for various crops and paddy ponding irrigation in land surface models, realistically modeling irrigation practices in the MLYP is challenging. Therefore, this study developed a subgrid-scale multi-crop demand-driven irrigation scheme and coupled it with the Weather Research and Forecasting-Noah (WRF-Noah) model, incorporating a new ponding submodule to simulate water and energy budget processes in paddy field subgrids. Two experiments (irrigation on and off) were designed to explore the impacts and mechanisms of irrigation on the regional climate of MLYP. Results indicate that the modified scheme improved the simulation accuracy of surface fluxes, soil moisture, and irrigation amount. Irrigation significantly increased latent heat flux, leading to domain-averaged cooling of 0.316 degrees C during spring and summer, while the irrigated subgrids reached 0.647 degrees C. Reduced planetary boundary layer height strengthened atmospheric stability and suppressed summer local convective precipitation over MLYP. Irrigation-induced anomalous anticyclone and large-scale circulation perturbation inhibited internal moisture convergence and external imports, reducing summer non-convective precipitation. Our study emphasizes the critical importance of realistic irrigation representation in climate studies and advances the mechanism-level understanding that water management modulates land-atmosphere interactions.

To explore the impact of the changes of agricultural cropping-breeding mode(ACBM) on the water quantity and quality of rivers, Four Lakes Main Channel(FLMC) in the Jianghan Plain was taken as the research object. Based on observation data from 2010 to 2023, the trend of annual water quantity and quality changes was analyzed. Remote sensing images and statistical yearbook data were used to identify the interannual area change characteristics of different land use types, and the characteristics of nitrogen(N) and phosphorus(P) point source load under the change of agricultural planting mode were analyzed, the influencing factors of N and P flux in FLMC were discussed，and redundancy analysis (RDA) was applied to explore the response of the main channel’s N and P concentration to the changes.The results indicate that: ①In the past 10 years, there has been a significant change in the water quantity and quality of the main channel, and the water quantity has shown an increasing trend. The N and P concentration in the water body first increases and then decreases before stabilizing; The seasonal changes in water quantity and quality within the year are significant, with water quantity and N and P concentration reaching peak in summer and autumn from May to September. ②The spatial differences in N and P concentrations in water are significant, with N and phosphorus concentrations increasing first and then decreasing from the beginning to the end of the main channel. ③The ACBM in the Four Lakes Basin has changed significantly, with the proportion of rice field cultivation increasing by 18.8%, and the N and P agricultural non-point source loads increasing by 4231t/a and 564t/a, respectively; dryland crops and freshwater aquaculture decreased by 14.7% and 2.1% respectively, while the N and P non-point source loads decreased by 2710t/a and 1634t/a , and the P decreased by 153t/a and 171t/a, respectively. ④The characteristics of N and P concentration changes in FLMC are significantly positively correlated with N and P non-point source load, with an explanatory power of 60.7% (*p<0.01). The freshwater aquaculture is the main sources of N and P non-point source load, with contribution rates of 50-60% and 6-30%, respectively. ⑤In addition, the N and P flux at typical cross-sections of FLMC is closely related with changes in agricultural cultivation patterns, the increase in N and P flux from 2010 to 2016 is related to the growth of freshwater aquaculture and rice field cultivation areas, while the decrease in N and P flux from 2016 to 2023 is related to the growth of freshwater aquaculture areas and dry crop.

Inland small waterbodies are important water sources for human, and are also the cornerstone for maintaining ecological environment stability. It is of great significance to carry out accurate, fast, and green efficient monitoring of inland water quality. However, there are almost no ground-based water quality monitoring points in small inland waterbodies, and unmanned aerial vehicles (UAV) low-altitude remote sensing provides opportunities for water quality retrieval in these regions. This study proposed a machine learning based water quality retrieval framework that integrated spatiotemporal information, feature engineering and intelligent optimization algorithms to retrieve total nitrogen (TN) in inland small waterbodies. The results showed that the RMSE of the training, validation, and test sets was 0.036-0.21 mg/L, R2 was 0.918-0.998, RPD was 3.483-20.052, indicating that the established framework has high accuracy. Furthermore, the established framework was applied to multispectral images obtained from six UAV flights, and it was found that the predicted quartiles and mean values of TN concentration in each research area were close to the measured, with an error of less than 20%. Therefore, it is considered that the established model framework has high transferability. Although there are still some uncertainties in the water quality retrieval model constructed in this study, such as significant differences in the retrieval results of various models in part of the waterbodies, the overall spatial distribution is consistent. This study has achieved a transformation of water environment monitoring from point to surface, which can provide reference for intelligent management of small waterbodies.

Study region: The Jianghan Plain (middle-lower Yangtze River, China) is a low-lying lake-river plain where shallow groundwater is strongly coupled to river stages and human water use, making it sensitive to large-reservoir regulation. Within this plain, our focus is the downstream acute-angle interfluve sector between the Yangtze River (mainstem regulated by the TGR) and its largest tributary, the Hanjiang River, where surface water, soil water, and shallow groundwater are highly coupled and therefore expected to show the most pronounced response to regulated river stages. Study focus: Our analyses are extracted for this Yangtze-Han interfluve focus region, which defines the fixed study-area mask used throughout the diagnostics. We quantify the spatiotemporal response of ZWT to the Three Gorges Reservoir (TGR) impoundment and assess how this response varies across hydrological year types (wet, normal, and dry). We drive a 0.05 degrees regional configuration of the Community Earth System Model version 2 (CESM2) coupled with the Community Land Model version 5 (CLM5) and the Model for Scale Adaptive River Transport (MOSART), using high-resolution meteorological forcing and China-specific crop/land-use/soil-bedrock datasets to simulate ZWT during 1990-2018. Hydrological year types are classified using a standardized runoff-anomaly approach (thresholds explicitly defined for reproducibility). We compare preimpoundment (1990-June 2003) and post-impoundment (July 2003-2018) periods using trend, changepoint, and spatial diagnostics. New hydrological insights for the region: The simulations reproduce the observed seasonal phase and spatial pattern of ZWT. Regionally, the long-term ZWT decline weakens after impoundment (about 8.78% at the regional mean) and a statistically significant changepoint is detected in April 2005, consistent with a lagged adjustment of the river-lake-aquifer system. Under a linear approximation, corresponding to an approximately 0.84 m cumulative water-table rise along the near-Yangtze corridor over 2004-2018. Hydrological year type strongly modulates the response: wet years show the largest affected area and the strongest seasonal-amplitude contraction, with pronounced deepening in summer-autumn. Spatially, ZWT deepening organizes into a narrow corridor near the Yangtze (112.78 degrees-113.12 degrees E / 29.48 degrees-29.62 degrees N), while localized shallowing clusters occur in the northern/northwestern plain (111.88 degrees-112.03 degrees E / 30.38 degrees-30.88 degrees N). These results highlight that regulated-flow impacts on groundwater are not uniform in space or climate state, supporting targeted monitoring and groundwater-risk zoning for the Jianghan Plain and other downstream alluvial plains.

Integrated rice-crayfish farming is an agricultural cultivation model that has become popular and developed rapidly in China in recent years. It integrates rice planting and crayfish farming into one system, boasting favorable environmental benefits and economic returns. In current context of global water scarcity, ensuring the sustainable development of rice-crayfish co-cultivation systems necessitates in-depth research on water consumption characteristics and water-saving potential of this agricultural model. In this study, an innovative nested tank model was developed to simulate the water balance of rice-crayfish fields. Three rice-crayfish field observations confirmed the high performance and good fit of the model, with a Nash efficiency coefficient (NSE), correlation coefficient (R2), relative error (epsilon), and root mean square error (RMSE) ranging from 0.81-0.97, 0.91-0.98, 2-4 %, and 15.8-39.2 mm, respectively. The epsilon between the simulated and observed total irrigation amounts ranged from 0.49-10.70 %. The developed model was applied to assess the water-saving potential and benefit-enhancing performance of 60 combinatorial schemes. These schemes were designed by varying three factors: five crayfish ditch area ratios (5 %, 10 %, 20 %, 30 %, and 40 %), four crayfish ditch excavation depths (0.8 m, 1 m, 1.5 m, and 2 m), and three irrigation schemes (FI1: shallow-shallow, FI2: shallow-deep, FI3: deepdeep), under three typical annual rainfall frequencies (15 %, 50 %, and 85 %). Through systematic evaluation of the simulation results, the optimal scheme was identified as 5 %-1.5-FI1. The irrigation quota of the optimal scheme was reduced by 54.4 % compared to the least favorable scheme (40 %-2-FI3), with a cost-benefit ratio of 1.6 times higher. The drainage volumes and the irrigation water productivities of the optimal and least favorable schemes showed no significant differences. Overall, the optimal scheme can reduce irrigation water consumption and improve economic benefits, providing a reference for irrigation management and field layout optimization of rice-crayfish co-culture fields.

Study region: The Honghu Lake (HHL) and Changhu Lake (CHL) in middle China. Study focus: Large-scale and high-precision estimation of water quality parameters (WQPs) is critical in explaining the spatiotemporal dynamics and clarifying their response to short-term hydrometeorological factors. Six machine learning models optimized by intelligent optimization algorithms (IOA-ML) were developed to retrieve WQPs using paired in situ measurements and near-synchronous Sentinel-2 reflectance (Rrs). Furthermore, the response of pixel-based WQPs to short-term hydrometeorological factors were explored by generalized additive model (GAM). New hydrological insights for the region: The results showed that R rs curves were significantly correlated with WQPs concentration, which provided a solid foundation for WQPs retrieval. The best IOA-ML model for total phosphorus (TP), total nitrogen (TN), and permanganate index (CODMn) was extreme gradient boosting optimized by genetic algorithm (GA-XGB), while that for dissolved oxygen (DO) and turbidity was categorical boosting regression optimized by GA (GACBR). Coefficient of determination (R2) of the best retrieval models for the test sets of TP, TN, turbidity, CODMn and DO were 0.545, 0.418, 0.794, 0.798, and 0.653, respectively. The best retrieval models were applied to two big inland lakes and revealed that TP, TN, CODMn, and turbidity in HHL increased rapidly from 2016 to 2022, especially during 2021-2022. 81.4 %91.5 % of the WQPs variations in HHL and 63.4 %-92 % in CHL can be explained by hydrometeorological factors.

Biochar has recently been widely used as a soil amendment. However, the interaction effects of biochar with irrigation management on soil water leakage and water use efficiency of paddy black soil remain unclear, which seriously restricts the production potential of black soil. Therefore, the purpose of this paper was to explore the response rule of water loss and water use efficiency of black soil under the coupling effects of biochar, irrigation amounts, and irrigation methods through column experiment, field experiment, and HYDRUS-AquaCrop coupling simulation. Biochar application rates, irrigation amounts, and irrigation methods were set at five levels (B = 0, 1.5, 3, 4.5, 6 kg·m−2), seven levels (I = 0, 60, 120, 180, 240, 300, 360 mm), and two levels (M, conventional irrigation and drip irrigation), respectively. The results showed that B and M had a significant coupling effect on water leakage loss (p < 0.05). Single factor B promoted water loss, but B and M inhibited water loss, which helps reduce water waste and environmental pollution. Compared with a single effect, the synergistic effect of B, I, and M on water consumption (ET), yield (Y), and water use efficiency (WUE) was better, increasing Y by 18.2%–57.9% and WUE by 17.1%–34.9%. Additionally, ET, Y, and WUE were also correlated with hydrological years, and this correlation works best in dry years. The maximum of Y and WUE in wet and normal years occurred in the ‘BDI6, 0 mm’ treatment (saving water and high yield), while that in dry years occurred in the ‘BDI6, 360 mm’ treatment (a stable yield). Therefore, the interaction effects of biochar and irrigation management should be comprehensively considered in black soil agricultural production to improve the agricultural potential of black soil and ensure food security.

Intensive irrigation and fertilization in rice cultivation have increasingly degraded water resources and aquatic ecosystems. The drainage water recycling system (DWRS) offers a promising strategy to improve water use efficiency and reduce nitrogen (N) and phosphorus (P) losses, yet its management effects remain poorly understood. Therefore, the objective was to investigate nutrient loss dynamics and evaluate the effectiveness of DWRS management by integrating field experiments with a modified SWAT model (SWAT-DWRS), aiming to develop management strategies suitable for rice-based irrigation areas. Experimental results showed that paddy fields, ditches, and ponds each contributed to nutrient removal. However, DWRS drainage might still threaten water quality when downstream standards were stricter than Class IV. Under reduced irrigation volume and expanded pond drainage area scenarios, TN and TP loads decreased by 17.05 % and 15.44 %. These reductions were over four times greater than the reduction in runoff, indicating significant potential for water quality improvement. Higher nutrient concentrations in irrigation water enhanced nutrient removal, suggesting full utilization of aquaculture drainage for irrigation. Notably, due to interactions among paddy fields, ditches, and ponds, nutrient removal did not increase linearly with higher maximum ponding water level (Hmax), diverging from previous findings. DWRS optimization by reducing irrigation, prioritizing pond water reuse, increasing Hmax (120 mm), and expanding pond drainage area (0.44) reduced runoff, TN, and TP loads by 24.44 %, 47.91 %, and 45.52 %, respectively. These results underscore the potential of optimized DWRS management to improve water use efficiency and water quality in rice-based irrigation areas.

To explore the coupling between agricultural farming models and surface water environmental in central China’s irrigation districts, this study focuses on the Four Lakes Basin within Jianghan Plain, a key grain-producing and ecological protection area. Integrating remote sensing images, statistical yearbooks, and on-site monitoring data, the study analyzed the phased characteristics of the basin’s agricultural pattern transformation, the changes in non-point source nitrogen and phosphorus loads, and the responses of water quality in main canals and Honghu Lake to agricultural adjustments during the period 2010~2023. The results showed that the basin underwent a significant transformation in agricultural patterns from 2016 to 2023: the area of rice-crayfish increased by 14%, while the areas of dryland crops and freshwater aquaculture decreased by 11% and 4%, respectively. Correspondingly, the non-point source nitrogen and phosphorus loads in the Four Lakes Basin decreased by 11~13%, and the nitrogen and phosphorus concentrations in main canals decreased slightly by approximately 2 mg/L and 0.04 mg/L, respectively; however, the water quality of Honghu Lake continued to deteriorate, with nitrogen and phosphorus concentrations increasing by approximately 0.46 mg/L and 0.06 mg/L, respectively. This indicated that the adjustment of agricultural farming models was beneficial to improving the water quality of main canals, but it did not bring about a substantial improvement in the sustainable development of Honghu Lake. This may be related to various factors that undermine the sustainability of the lake’s aquatic ecological environment, such as climate change, natural disasters, internal nutrient release from sediments, and the decline in water environment carrying capacity. Therefore, to advance sustainability in this basin and similar irrigation districts, future efforts should continue optimizing agricultural models to reduce nitrogen/phosphorus inputs, while further mitigating internal nutrient release and climate disaster risks, restoring aquatic vegetation, and enhancing water environment carrying capacity.

Achieving reasonable and effective nutrient management requires a comprehensive framework that seamlessly integrates modelling outcomes for both present conditions and future projections. Due to the diversity of basin attributes and variations of removal processes in large-scale basins, it remains difficult to understand nutrient budgets in basins with complex stream networks. Additionally, external environmental changes induced by climate change and socioeconomic development, will also bring uncertainty to water management policies based on current assessments. This study develops a new holistic framework based on the SPARROW (SPAtially Referenced Regression On Watershed attributes) model coupling with the interaction of climate change and socioeconomic development. The framework can integrate multi-source information and model present and future scenarios to evaluate the nutrient status comprehensively. The application of this methodology is demonstrated through a case study in the Danjiangkou Reservoir basin (DJKRB) in China. Findings revealed that atmospheric deposition emerged as the predominant total nitrogen (TN) source in the DJKRB, contributing over 60% on average; while total phosphorus (TP) sources were more diversified, with untreated urban wastewater being a significant contributor, accounting for roughly 37%. The analysis of future uncertainties based on scenario simulations and sensitivity analyses further shows the need for prioritising efforts to mitigate atmospheric nitrogen pollution and promote precipitation-induced runoff management within the DJKRB. This study not only serves as a scientific basis for nutrient modelling in the context of evolving environmental conditions but also proposes a practical methodological framework for water resources management in expansive basins through a real-world case study in the DJKRB.

Biochar’s benign effects on agricultural production have been demonstrated. Still, no consistent conclusions have been drawn on the impact of biochar-amended paddy fields on carbon sequestration, gas emission reduction, and efficiency enhancement in typical cropping areas in the middle Yangtze River. A field experiment using five dosages of biochar (CK, BC1.5, BC3, BC4.5, and BC6) at 0, 1.5, 3, 4.5, and 6 kg·m−2 was conducted at the Hubei Irrigation Experiment Center Station, Jingmen City, Hubei Province, China, to investigate the effects of biochar on carbon sequestration, greenhouse gas emissions, and agricultural efficiency in paddy in the middle Yangtze River Region. This study showed that the optimal biochar dosage was 4.5 kg·m−2 (BC4.5). Biochar significantly improved soil properties, increased rice yield by 26.4–61.4%, and enhanced water use efficiency (WUE) and economic profit (EP) by 32.0–83.7% and −8.0–48.6%, respectively. Biochar increased soil carbon sequestration (SCS) and carbon pool management index (CPMI) by 23.0–198.3% and 22.9–71.5%, respectively. Biochar also reduced greenhouse gas emission intensity (GHGI), global warming potential (GWP), and emissions of CO2, CH4, and N2O. Furthermore, structural equation modeling (SEM) indicated that soil organic carbon (SOC), in addition to the “biochar” influence factor, was a key positive influence factor for SCS, CPMI, and EP. Another major positive factor for GWP was silt, and for WUE it was saturated hydraulic conductivity, while TN and SOC were the major negative variables for GHGI. In summary, biochar demonstrated outstanding carbon sequestration and emission reduction impacts while ensuring crop production growth and efficiency improvement. The results provide a research basis for safeguarding food security and mitigating climate warming in the middle Yangtze River region.

Within a river catchment, the relationship between pollutant load migration and its related factors is nonlinear generally. When neural network models are used to identify the nonlinear relationship, data scarcity and random weight initialization might result in overfitting and instability. In this paper, we propose an averaged weight initialization neural network (AWINN) to realize the multi-index integrated prediction of a pollutant load under data scarcity. The results show that (1) compared with the particle swarm optimization neural network (PSONN) and AdaboostR models that prevent overfitting, AWINN improved simulation accuracy significantly. The R2 in test sets of different pollutant load models reached 0.51–0.80. (2) AWINN is effective in overcoming instability. With more hidden layers, the stability of the models’ outputs was stronger. (3) Sobol sensitivity analysis explained that the main influencing factors of the whole process were the flows of the catchment inlet and outlet, and main factors changed across seasons. The algorithm proposed in this paper can realize stably integrated prediction of pollutant load in the catchment under data scarcity and help to understand the mechanism that influences pollutant load migration.

Accurate forecasting of water quality variables in river systems is crucial for relevant administrators to identify potential water quality degradation issues and take countermeasures promptly. However, pure data-driven forecasting models are often insufficient to deal with the highly varying periodicity of water quality in today's more complex environment. This study presents a new holistic framework for time-series forecasting of water quality parameters by combining advanced deep learning algorithms (i.e., Long Short-Term Memory (LSTM) and Informer) with causal inference, time-frequency analysis, and uncertainty quantification. The framework was demonstrated for total nitrogen (TN) forecasting in the largest artificial lakes in Asia (i.e., the Danjiangkou Reservoir, China) with six-year monitoring data from January 2017 to June 2022. The results showed that the pre-processing techniques based on causal inference and wavelet decomposition can significantly improve the performance of deep learning algorithms. Compared to the individual LSTM and Informer models, wavelet-coupled approaches diminished well the apparent forecasting errors of TN concentrations, with 24.39%, 32.68%, and 41.26% reduction at most in the average, standard deviation, and maximum values of the errors, respectively. In addition, a post-processing algorithm based on the Copula function and Bayesian theory was designed to quantify the uncertainty of predictions. With the help of this algorithm, each deterministic prediction of our model can correspond to a range of possible outputs. The 95% forecast confidence interval covered almost all the observations, which proves a measure of the reliability and robustness of the predictions. This study provides rich scientific references for applying advanced data-driven methods in time-series forecasting tasks and a practical methodological framework for water resources management and similar projects.

Massive irrigation across eastern China (EC) could reduce extreme heat by altering energy and water budgets. However, the irrigation effect on increasing humidity has often been missed, especially in humid regions, leaving its effect and mechanism on extreme humid heat (EHH) poorly characterized. We analyzed assemblies of observations and performed regional simulations with more detailed irrigation scheme to explore the irrigation impacts and potential mechanisms on EHH. We find that irrigation in EC, despite having a cooling effect of about 0.2-0.6 degrees ${}<^>{\circ}$C, leads to an increase of about 0.4-0.8 degrees ${}<^>{\circ}$C in EHH, with a more intense impact on the Middle-Lower Yangtze Plain (MLYP) by 0.9 degrees ${}<^>{\circ}$C. Cooling effect induced by increased latent heat fluxes through irrigation contributes to air deposition, which lowers boundary layer height, raises near-surface moist enthalpy, and ultimately exacerbates the EHH. Results mechanistically emphasized irrigation impacts on EHH and highlighted the necessity of improving irrigation modeling reality.

Dissolved oxygen (DO) is an essential indicator for assessing water quality and managing aquatic environments, but it is still a challenging topic to accurately understand and predict the spatiotemporal variation of DO concentrations under the complex effects of different environmental factors. In this study, a practical prediction framework was proposed for DO concentrations based on the support vector regression (SVR) model coupling multiple intelligence techniques (i.e., four data denoising techniques, three feature selection rules, and four hyperparameter optimization methods). The holistic framework was tested using a data matrix (17,532 observation data in total) of 12 indicators from three vital water quality monitoring stations of the longest inter-basin water diversion project in the world (i.e., the Middle-Route of the South-to-North Water Diversion Project of China), during the year 2017 to 2020 period. The results showed that the framework we advocated for could successfully and accurately predict DO concentration variations in different geographical locations. The model used the “wavelet analysis–LASSO regression–random search–SVR” combination of the Waihuanhe station has the best prediction performance, with the Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2) values of 0.251, 0.063, 0.190, and 0.911, respectively. The combined methods using feature selection and hyperparameter optimization techniques can significantly promote the robustness and accuracy of the prediction model and can provide a new universal and practical way of investigating and understanding the environmental drivers of DO concentration variations. For the water quality management department, this proposed comprehensive framework can also identify and reveal the key parameters that should be concerned and monitored under different environmental factors change. More studies in terms of assessing potential integrated water quality risk using multi-indicators in mega water diversion projects and/or similar water bodies are required in the future.

The impact of the operation of inter-basin water diversion projects on the integrity and stability of regional ecosystems cannot be ignored. In this study, water quality samplings were conducted monthly at 16 national monitoring sites in the mid-downstream of the Hanjiang River (HJR, the downstream of the water source of the South-to-North Water Diversion Project of China) over 3 years, covering seven physiochemical water quality indicators and six heavy metal elements. The water quality index (WQI) and multivariate statistical techniques were introduced to comprehensively evaluate water quality status and understand the corresponding driving factors of water quality variations. The heavy metal risks were evaluated using the Nemerow Pollution Index (Pn), the Heavy Metal Pollution Index (HPI), and the human health risk assessment model. The results showed that after the operation of the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC), water quality in the mid-downstream of the HJR was generally at a “good” status, with the average WQI of 86.37, showing no water quality deterioration trends. The operation of the MRSNWDPC did significantly decrease the monthly flow in the HJR by about 4.05–74.27%, and the flow variation processes also became more stable than before. Most water quality indicators and WQIs have no correlations with the flow and water level changes. The human health risks of all heavy metal elements caused by dermal exposure and ingestion pathways increased over time. The average individual health risk caused by carcinogenic heavy metal Cr was the highest. Chromium is the major carcinogenic factor and should be a critical indicator to pay special attention to for water risk management in the HJR. This study provides a scientific reference for the water quality safety management of HJR under the influence of a water diversion project.

As the second largest reservoir in China, the Danjiangkou Reservoir (DJKR) serves as the water source of the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC), i.e., the currently longest (1273 km) inter-basin water diversion project in the world, for more than eight years. The water quality status of the DJKR basin has been receiving worldwide attention because it is related to the health and safety of >100 million people and the integrity of an ecosystem covering >92,500 km2. In this study, basin-scale water quality sampling campaigns were conducted monthly at 47 monitoring sites in river systems of the DJKRB from the year 2020 to 2022, covering nine water quality indicators, i.e., water temperature (WT), pH, dissolved oxygen (DO), permanganate index (CODMn), five-day biochemical oxygen demand (BOD5), ammonia nitrogen (NH3-N), total phosphorus (TP), total nitrogen (TN), and fluoride (F-). The water quality index (WQI) and multivariate statistical techniques were introduced to comprehensively evaluate water quality status and understand the corresponding driving factors of water quality variations. An integrated risk assessment framework simultaneously considered intra and inter-regional factors using information theory-based and the SPA (Set-Pair Analysis) methods were proposed for basin-scale water quality management. The results showed that the water quality of the DJKR and its tributaries stably maintained a "good" status, with all the average WQIs >60 of river systems during the monitoring period. The spatial variations of all WQIs in the basin showed significantly different (Kruskal-Wallis tests, P < 0.01), while no seasonal differences were found. The increase in built-up land use and agricultural water consumption revealed the highest contributions (Mantel's r > 0.5, P < 0.05) to the rise of nutrient loadings of all river systems, showing the intensive anthropogenic activities can eclipse the power of natural processes on water quality variations to some extent. The risks of specific sub-basins that may cause water quality degradation on the MRSNWDPC were effectively quantified and identified into five classifications based on transfer entropy and the SPA methods. This study provides an informative risk assessment framework that was relatively easy to be applied by professionals and non-experts for basin-scale water quality management, thus providing a valuable and reliable reference for the administrative department to conduct effective pollution control in the future.

Total phosphorus (TP) concentration is high in countless small inland waterbodies in Hubei province, middle China, which is threating the water environment. However, there are almost no ground-based water quality monitoring points in small inland waterbodies, because the cost of time, labor, and money is high and it does not meet the needs of spatiotemporal dynamic monitoring. Remote sensing provides an effective tool for TP concentration monitoring spatiotemporally. However, monitoring the TP concentration of small inland waterbodies is challenging for satellite remote sensing due to the inadequate spatial resolution. Recently, unmanned aerial vehicles (UAV) have been applied to quantitatively retrieve the spatiotemporal distribution of TP concentration without the challenges of cloud cover and atmospheric effects. Although state-of-the-art algorithms to retrieve TP concentration have been improved, specific models are only used for specific water quality parameters or regions, and there are no robust and reliable TP retrieval models for small inland waterbodies at this time. To address this issue, six machine learning methods optimized by intelligent optimization algorithms (IOA-ML models) have been developed to quantitatively retrieve TP concentration combined with the reflectance of original bands and selected band combinations of UAV multispectral images. We evaluated the performances of models in terms of coefficient of determination (R2), root mean squared error (RMSE), and residual prediction deviation (RPD). The results showed that the R2 of the six IOA-ML models for training, validation, and test sets were 0.8856–0.984, 0.8054–0.8929, and 0.7462–0.9045, respectively, indicating the methods had high precision and transferability. The extreme gradient boosting optimized by genetic algorithm (GA-XGB) performed best, with the highest precision for the validation and test sets. The spatial distribution of TP concentration of each flight derived from different models had similar distribution characteristics. This paper provides a reference for promoting the intelligent and automatic level of water environment monitoring in small inland waterbodies.

城乡结合带是流域复杂多样污染物的主要源汇区.科学识别该类区域的污染物入河湖特征,并根据河湖水功能区划等环境保护目标要求,优化区域城乡发展规模与种养结构,对河湖保护和综合治理具有重要意义.以四湖流域中下区这一典型城乡结合带为研究对象,采用改进输出系数法与浓度法,分别计算2008-2018年各市县的非点源与点源污染物入河量,得到污染物入河时空分布规律与特征,探明洪湖市农业种养是主要贡献源区之一.通过建立区间模糊多目标规划模型,对低、高两种节水控污水平下2030年种养结构及城乡规模进行了优化.结果表明:①2008-2018年研究区TP、COD入河量呈增高趋势,分别增加3.86％、11.11％,而NH3-N减少14.91％,TN减少0.4％,各类污染物入河总量最大值来源由监利市转移至洪湖市,研究区污染主控来源由城市污染逐渐转为农村污染,应加强农村面源污染的治理;②2030年研究区优化后节水减排效果显著,在高节水控污水平下荆州区域和潜江区域的万元GDP用水量比2018年最多下降42.2％和61.7％,TN、TP、COD、NH3-N比2018年最多分别削减47.9％、50.5％、56.2％、57.3％;③2030年研究区内水田种植应转变为旱作或虾稻种养模式,畜禽养殖量缩减20.9％,水产养殖量增加8.9％,农村人口缩减48.5％～49.4％,城镇人口增加76.2％～77.7％,工业增加值提高183.4％～183.9％.研究结果为洪湖水环境治理和四湖流域污染物入河总量控制提供了科学依据.

河湖水系连通性评价对实施区域水网连通战略,提高区域水资源统筹调配和承载能力,修复和改善水生态环境具有重要意义.以江汉平原河湖水系为研究对象,采用考虑闸站阻隔效应的改进图论法与基于多维连通机制的层次分析法,从网络结构和连通功能两方面分析评价了河湖水系连通现状与影响因素.结果表明:①研究区水网结构连通性得分为0.7172,处于"较好"等级,但空间分布不均衡,西部和东部连通性较高,中部连通性较低,通顺河流域连通性高于四湖流域;②研究区水网功能连通性得分为0.6885,处于"较好"等级,主要受区域灌排闸站数量较多、控制程度较高、闭合时间较长等不利因素影响,局部地区如四湖总干渠、通顺河以及洪湖等水流不畅,连通性较低;③结构和功能连通性评价结果基本一致,评价方法可靠性及适用性较强,可为江汉平原水网工程体系调整与生态河湖连通系统建设提供一定参考.